Optimizing Asphalt Compaction: Intelligent Compaction Roller Frequency and Machine Learning Prediction of In-Place Density

dc.contributor.author	Ahmad, Kamal Nasir
dc.contributor.author	Xianhua, Chen
dc.contributor.author	Lu, Qing
dc.date.accessioned	2026-08-20T21:28:47Z
dc.date.available	2026-08-20T21:28:47Z
dc.date.copyright	2026/06/10
dc.date.issued	2026
dc.identifier.other	JCCEE5.CPENG-7492.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314523
dc.description.abstract	AbstractTraditional destructive methods for assessing asphalt pavement compaction, such as core sampling, are time-consuming, costly, and provided limited spatial coverage. Nondestructive approaches are essential for real-time quality control (QC) and ...
dc.publisher	American Society of Civil Engineers
dc.title	Optimizing Asphalt Compaction: Intelligent Compaction Roller Frequency and Machine Learning Prediction of In-Place Density
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	5
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-7492
dc.journal.fristpage	04026075-1
dc.journal.lastpage	04026075-13
dc.page	13
dc.tree	Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 005
dc.contenttype	Fulltext